Top Tweets for #mBx
The Demon Mark NFT Raffle: Combine & Win Big winners are finally here! 🏆
Check if you are one of the lucky ones taking home Astronaut Koongya NFTs, Demon Meliodas NFTs, Game Coupons, and a share of the 10,000 #MBX prize pool!
See the full winner list and claim instructions here:
👉 https://t.co/4VyLCoGCbZ
Celebrating @PaesaniLab #PhD #16! 🎓
Huge congratulations to Dr. Henry Agnew, who successfully defended his thesis today on #datadriven #manybody simulations of ions in bulk water and at interfaces! 🎉
Henry also played a key role in making our #MBX software faster 🚀 and more accessible to everyone. 🤝 But even more importantly, over the past five years, he has always been there to help everyone around him.
He has made our science and our group better. We could not be prouder! 🙌
@UCSanDiego @UCSDPhySci @UCSDChem @HDSIUCSD @SDSC_UCSD
🚨 New paper out today in APLComputational Physics!
👉 https://t.co/uF2Ykg9cFt
Our latest work on #MBX shows how memory-aware SIMD vectorization can significantly accelerate #datadriven #manybody simulations. ⚡
By optimizing the n-body kernels for modern CPUs, we achieve nearly 4× speedups with AVX2 and 8× with AVX-512, translating into up to 3.2× faster MB-pol #water simulations. 🚀
The improvements are already available in #MBX v1.4 💻:
https://t.co/Y98JJqdU1O
Besides making CCSD(T)-lelvel #datadriven #manybody simulations faster and more accessible, these developments provide a strong foundation for the GPU implementation of MBX that is now underway. Stay tuned! 🏄♀️
Huge shoutout to Shishir and Ethan for leading this work, and big thanks to Chris for the collaboration! 🙌
Big thanks also to @NSF for supporting this research and to @ACCESSforCI for providing computational resources! 🙏
@UCSanDiego @UCSDPhySci @UCSDChemBiochem @HDSIUCSD @SDSC_UCSD
MBX x Bybit Airdrop Trade & Earn!
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Only the first 700 participants win, so hurry! (FCFS) 🏃♂️
📅August 10, 2026, 21:00 - End (UTC+9)
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3️⃣ Screenshot your trade history (min $10)
4️⃣ Submit the Google Form with your info & screenshot
📝 Submit here: https://t.co/6NRLd7y7tg
🔗 Trade here: https://t.co/Hikjru5FrF
Create your own The Seven Deadly Sins NFT!
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@sohan269 @MSX_CN @msxcom ⚠️ MBX Exchange Warning: Users report account suspensions after deposits. Deposited funds may become inaccessible. Deposit at your own risk.
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My Free 7DS Character Meliodas on NFT Adventure by @MARBLEXofficial is rdy!
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@MARBLEXofficial
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ゲーム概要
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・ジャンル：ミッション型NFTコレクティブル
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注目ポイント
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トークン / 経済設計
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開発元
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・運営：MARBLEX (@MARBLEXofficial)
・実績：大手ゲーム会社ネットマーブルが手掛けるブロックチェーンエコシステム。確かな実績と信頼性があります。
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参加方法
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初期NFTの獲得も上位NFTへの交換も枠に限りがあります！
今すぐ下のリンクからミッションを開始しましょう👇
https://t.co/ljNFOe4Ggj
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Ready for the next chapter? MARBLEX opens NFT Adventure Season 2 pools & Meliodas staking at 300% APR. Don't miss your chance to join The Seven Deadly Sins on-chain! 🔥 #MBX
NFT Adventure Season 2 is finally here! 🎉
To celebrate, we are officially opening the SWAP and CLAIM pools, along with our new STAKING service for the legendary Demon Meliodas! 😈✨
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Getting ready to speedrun those daily quests for Hawk Points and summon my free character on July 30th! ✨
#MBX #NFTAdventure #7DS
📢 The wait is finally over!
NFT Adventure Season 2 ft. The Seven Deadly Sins is officially launching on July 30! ⚔️🔥
Get ready to step into a web3 Britannia and embark on a brand-new journey. 🛡️
Set your alarms for July 30, 2026 at 14:00 (UTC+9)! ⏰
👉 Check out the full details here:https://t.co/8eAxoOPUaB
🚨 We are excited to announce a major speedup for #MBX, our software for #datadriven #manybody simulations!
A new memory-aware SIMD implementation accelerates the evaluation of the n-body kernels by nearly 4× with AVX2and 8× with AVX-512, increasing the overall speed of MB-pol #water simulations by up to 3.2×.
These improvements are now available in #MBX v1.4:
👉 https://t.co/Y98JJqdU1O
New to #MBX? Check out our video tutorials:
🎬 https://t.co/m4E3KsqB09
The result? On 32 @AMD CPU cores, MBX/MB-pol is about 1.7× faster than foundation ML models running on an @NVIDIA H200 GPU in our #water benchmarks, even though the CPU costs roughly 10× less than the GPU. 😉
An important distinction: our #datadriven #manybody potentials currently target CCSD(T)-level accuracy for aqueous systems through a combination of ML short-range interactions and explicit many-body #physics, while foundation ML models currently span a much broader chemical space with DFT-level accuracy. Progress toward expanding the reach of #datadriven #manybody potentials is ongoing. 😎
And we are not done yet: further CPU optimizations are being finalized, and development of a GPU version of #MBX is already underway. Stay tuned! 🏄♀️
Huge shoutout to Shishir and Ethan for leading these developments! 🙌
Big thanks to @NSF for supporting this research and to @ACCESSforCI for providing the computational resources that made it possible! 🙏
@UCSanDiego @UCSDPhySci @UCSDChemBiochem @HDSIUCSD @SDSC_UCSD
🚨 Our latest preprint is out at @ChemRxiv!
👉 https://t.co/voiivrsU4A
In this work, we tackle a key software-engineering bottleneck inside #MBX: the repeated evaluation of permutationally invariant polynomial (#PIP) energy/gradient kernels. 🖥️
#PIPs are central to #datadriven #manybody potentials because they represent short-range n-body quantum effects with CCSD(T)-level accuracy. But in molecular dynamics, #PIPs and their gradients are evaluated thousands of times at every timestep, so memory layout and code generation matter enormously. ⏰
We introduce a memory-aware, #SIMD-enabled code-generation pipeline for #MBX. Starting from MB-Fit/Maple-generated #PIP routines, the new workflow:
1⃣ identifies and precomputes duplicate subexpressions
2⃣ represents the straight-line program as a directed acyclic graph (DAG)
3⃣ reorders statements to shrink the live working set
4⃣ reuses temporary memory locations as soon as intermediates become inactive
5⃣ reformats batches of inputs for AVX2 and AVX-512 #SIMD vectorization
Using the MB-pol 3-body #PIP as a demanding test case, this reduces the working set by 84.5%. Even in the AVX-512 routine, which evaluates 8 PIPs per call, the optimized implementation uses 64.3% less temporary memory than the original scalar kernel. 😎
The payoff is substantial: #PIP evaluation reaches nearly ideal #SIMD speedups, ~4× with AVX2 and ~8× with AVX-512. In full MB-pol simulations, the optimized kernels improve #MBX throughput by ~1.4–3.2× depending on the instruction set and #OpenMP settings. 🏄♀️
This #MBX improvement moves #datadriven #manybody potentials closer to the computational efficiency of conventional force fields while also enabling CCSD(T)-level accuracy for simulations across phases. 🌊
And there is more ahead: these working-set and memory-layout optimizations are directly relevant for #GPUs, where memory traffic is often the limiting factor. Stay tuned! 🚀
Huge congratulations to Shishir and Ethan for making this happen! 🙌
Big thanks to @NSF for funding this software development! 🙏
✅ SIMD code: https://t.co/nnHll5U9Qq
✅ MBX: https://t.co/eY2zrSvDUF
✅ Google group: https://t.co/EFiOa7qVMJ
✅ Data-driven many-body simulations: https://t.co/bVIx3HHZn3
✅ Video tutorials: https://t.co/m4E3Ksq3aB
@UCSanDiego @UCSDPhySci @UCSDChemBiochem @HDSIUCSD @SDSC_UCSD
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